This is a really interesting example of how agentic AI starts to become more meaningful when it’s tied to an actual business workflow rather than just a demo.
What I particularly like about the combination of LangChain, MCP, RAG, Ollama, and FastAPI is that each piece has a practical role—retrieving relevant information, connecting tools, running the model, and exposing everything through an application layer. Insurance also seems like a great domain for this because there’s so much structured and unstructured information to work with.
The part I’d be most curious about in a real-world deployment is how you handle things like incorrect retrievals, outdated policy information, and keeping humans involved when an agent’s decision could have real consequences. That’s where these systems become much more interesting than a simple chatbot.
Great project and a nice illustration of what a practical agentic AI stack can look like.
This is a really interesting example of how agentic AI starts to become more meaningful when it’s tied to an actual business workflow rather than just a demo.
What I particularly like about the combination of LangChain, MCP, RAG, Ollama, and FastAPI is that each piece has a practical role—retrieving relevant information, connecting tools, running the model, and exposing everything through an application layer. Insurance also seems like a great domain for this because there’s so much structured and unstructured information to work with.
The part I’d be most curious about in a real-world deployment is how you handle things like incorrect retrievals, outdated policy information, and keeping humans involved when an agent’s decision could have real consequences. That’s where these systems become much more interesting than a simple chatbot.
Great project and a nice illustration of what a practical agentic AI stack can look like.